Conventionally, there is an apparatus that finds a subspace of standard motion by means of principal component analysis on a multidimensional motion feature amount indicating a feature of motion, and performs analysis of motion subject to comparison based on distance between a found standard motion subspace and a motion feature amount of a motion subject to comparison (see Patent Literature 1, for example).
An apparatus described in Patent Literature 1 (hereinafter referred to as “conventional apparatus”) detects abnormal motion from motion subject to comparison using a cubic higher-order local autocorrelation (CHLAC) feature, this being a feature extracted from a moving image. Specifically, a conventional apparatus extracts motion feature amount feature data from an accumulated plurality of standard motions, and generates a subspace based on a principal component vector (hereinafter referred to simply as “subspace”) from the extracted plurality of feature data by means of principal component analysis. Then the conventional apparatus also extracts motion feature amount feature data in a similar way from motion subject to comparison, and if the distance between extracted feature data and the subspace is larger than a predetermined value, determines that motion subject to comparison to be abnormal.
A conventional apparatus of this kind learns standard motion statistically, and can therefore perform abnormal motion detection appropriate for a monitoring target, without particularly performing standard motion definition in the design stage.
In order to prevent abnormal motion determination leakage as far as possible, a feature data cumulative contribution ratio (hereinafter referred to as “contribution degree”) used when generating a subspace should be set high. A contribution degree is an index value indicating to what extent a generated subspace (principal component vector) explains original feature data. On the other hand, in order to prevent erroneous determination of standard motion, a feature data contribution degree used in principal component analysis should be set low. This is because variation of standard motion not included in original feature data can be absorbed if the contribution degree is low.
That is to say, an optimal contribution degree value depends on a monitoring target and required detection accuracy (hereinafter referred to as “analysis conditions”). Consequently, it is desirable for a contribution degree to be set to a value that suits analysis conditions.
Also, when motion analysis is performed in a factory, for example, it is assumed that camera installation locations are changed frequently in accordance with the work to be analyzed. Therefore, it is desirable for an apparatus that performs analysis of motion subject to comparison by means of principal component analysis (hereinafter referred to as “motion analysis apparatus”) to allow a contribution degree that suits analysis conditions to be set as easily as possible.